Free neural network java software for linux

Java Neural Network Trainer is a neural network trainer with the ability to easily add new training algorithms and training patterns.

Java Neural Network Trainer project includes a parallel training graphical interface where you can view each trainer working in real-time in parallel.

Java Network Stack provides a Java library for research oriented network programming.

Java Network Stack is a library used by the DIMES project to create new internet measurements. It provides a clean API for packet manipulation, send, receive, filter, and analysis. It is a unification of raw socket capabilities, MAC level networkingm, and IPv6 capabilities.

UltraGetopt for Java is the Java imagening of UltraGetopt. The project provides the features and flexibility of UltraGetopt in a way that is more suited to the Java environment.
Main features:
- Supports MS-DOS formatted option strings (e.g. /option:arg)
- Provides parsing and error message compatibility with getopt from the GNU, (Open)BSD, and Mac OS
- Supports first-longest-matching for options
- Provides many configurable behaviors

Fast Artificial Neural Network Library implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks.
Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, Python, Delphi and Mathematica bindings are available.
Main features:
- Multilayer Artificial Neural Network Library in C
- Backpropagation training (RPROP, Quickprop, Batch, Incremental)
- Evolving topology training which dynamically builds and trains the ANN (Cascade2)
- Easy to use (create, train and run an ANN with just three function calls)
- Fast (up to 150 times faster execution than other libraries)
- Versatile (possible to adjust many parameters and features on-the-fly)
- Well documented (An easy to use reference manual, a 50+ page university report describing the implementation considerations etc. and an introduction article)
- Cross-platform (configure script for linux and unix, dll files for windows, project files for MSVC++ and Borland compilers are also reported to work)
- Several different activation functions implemented (including stepwise linear functions for that extra bit of speed)
- Easy to save and load entire ANNs
- Several easy to use examples (simple train example and simple test example)
- Can use both floating point and fixed point numbers (actually both float, double and int are available)
- Cache optimized (for that extra bit of speed)
- Open source (licenced under LGPL)
- Framework for easy handling of training data sets
- Graphical Interface
- C++ Bindings
- PHP Extension
- Python Bindings
- Delphi Bindings
- .NET Bindings
- Mathematica Extension
- Octave Extension
- Ruby Bindings
- Pure Data Bindings
- Debian package

OpenGL for Java, formerly known as GL4Java, supports Java with a native OpenGL mapping.
The OS native OpenGL functionality is avaiable from Java for many OS: Linux/GNU+XFree86, Unix/X11, MacOS, Win32.
Enhancements:
- removed the hack (which I put in sometime ago to get VooDoo3 working) that forced gl4java to choose a double buffered pixel format (the demos that used single buffer did not work) (thanks JYB)
- fixed the tessdemo and tesswind applets (thanks JYB).

lisp-network-server is a simple framework for writing Common Lisp network applications.

lisp-network-server framework takes care of listening on the network, accepting the connection and starting a new thread with handler functions of your network aware application.

AI::NeuralNet::BackProp is a simple back-prop neural net that uses Deltas and Hebbs rule.

SYNOPSIS

use AI::NeuralNet::BackProp;
# Create a new network with 1 layer, 5 inputs, and 5 outputs.
my $net = new AI::NeuralNet::BackProp(1,5,5);

# Add a small amount of randomness to the network
$net->random(0.001);

# Demonstrate a simple learn() call
my @inputs = ( 0,0,1,1,1 );
my @ouputs = ( 1,0,1,0,1 );

print $net->learn(@inputs, @outputs),"n";

# Create a data set to learn
my @set = (
[ 2,2,3,4,1 ], [ 1,1,1,1,1 ],
[ 1,1,1,1,1 ], [ 0,0,0,0,0 ],
[ 1,1,1,0,0 ], [ 0,0,0,1,1 ]
);

# Demo learn_set()
my $f = $net->learn_set(@set);
print "Forgetfulness: $f unitn";

# Crunch a bunch of strings and return array refs
my $phrase1 = $net->crunch("I love neural networks!");
my $phrase2 = $net->crunch("Jay Lenno is wierd.");
my $phrase3 = $net->crunch("The rain in spain...");
my $phrase4 = $net->crunch("Tired of word crunching yet?");

# Make a data set from the array refs
my @phrases = (
$phrase1, $phrase2,
$phrase3, $phrase4
);

# Learn the data set
$net->learn_set(@phrases);

# Run a test phrase through the network
my $test_phrase = $net->crunch("I love neural networking!");
my $result = $net->run($test_phrase);

# Get this, it prints "Jay Leno is networking!" ... LOL!
print $net->uncrunch($result),"n";

AI::NeuralNet::BackProp is the flagship package for this file. It implements a nerual network similar to a feed-foward, back-propagtion network; learning via a mix of a generalization of the Delta rule and a disection of Hebbs rule. The actual neruons of the network are implemented via the AI::NeuralNet::BackProp::neuron package